Short-Term Load Forecasting Model For Power System Based on Complementation of Fuzzy-Rough Set Theory And BP Neural Network

2007 ◽  
Author(s):  
Peng Chen ◽  
Qing Zhang ◽  
Yujin Li ◽  
Jian Chen ◽  
Jianping Zhang
Keyword(s):  
Neural Network ◽  
Set Theory ◽  
Rough Set ◽  
Bp Neural Network ◽  
Rough Set Theory ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Short Term ◽  
Fuzzy Rough Set ◽  
Short Term Load Forecasting

scholarly journals Short-Term Load Forecasting of Power System Based on Improved BP Neural Network

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Power System ◽  
Bp Neural Network ◽  
Normal Load ◽  
Load Forecasting ◽  
System Optimization ◽  
Forecasting Model ◽  
Short Term ◽  
Short Term Load Forecasting

Power system load is a stochastic and non-stationary process. Due to the influence of various factors, some bad data may exist in the load observation value. These data are mixed into the normal load data to participate in the training of neural network, which seriously affects the accuracy of load forecasting. Short-term load forecasting is the basis of power system operation and analysis, improving the precision of load forecasting is an important means to ensure the scientific decision-making of power system optimization. In order to improve the precision of short term load forecasting in power system, a short-term load forecasting model based on genetic algorithm is proposed to optimize BP neural network. Firstly, using genetic algorithm to optimize the initial weights and thresholds of BP neural network to improve the prediction accuracy of BP neural network; Through the comparison and analysis before and after the model optimization, the experimental results with smaller prediction error were obtained. The simulation results show that the short-term load forecasting model established by this method has faster convergence rate and higher prediction precision.

BP neural network with rough set for short term load forecasting

2009 ◽  
Vol 36 (1) ◽  
pp. 273-279 ◽  
Author(s):  
Zhi Xiao ◽  
Shi-Jie Ye ◽  
Bo Zhong ◽  
Cai-Xin Sun
Keyword(s):  
Neural Network ◽  
Rough Set ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting

A Method Based on Wavelet Neural Network for Power System Short Term Load Forecasting

2014 ◽  
Vol 521 ◽  
pp. 303-306 ◽  
Author(s):  
Hong Mei Zhong ◽  
Jie Liu ◽  
Qi Fang Chen ◽  
Nian Liu
Keyword(s):  
Neural Network ◽  
Power System ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Wavelet Neural Network ◽  
Signal Spectrum ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Load Sequence ◽  
Daily Load

The short-term load of Power System is uncertain and the daily-load signal spectrum is continuous. The approach of Wavelet Neural Network (WNN) is proposed by combing the wavelet transform (WT) and neural network. By the WT, the time-based short-term load sequence can be decomposed into different scales sequences, which is used to training the BP neural network. The short-term load is forecasted by the trained BP neural network. Select the load of a random day in Lianyungang to study, according to the numerical simulation results, the method proves to achieve good performances.

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